forked from huawei/mindspore2022
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
# Copyright 2019 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.train import Model
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from mindspore.context import ParallelMode
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from mindspore.nn import PReLU
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from tests.dataset_mock import MindData
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context.set_context(mode=context.GRAPH_MODE)
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class Dataset(MindData):
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def __init__(self, predict, label, length=3, input_num=2):
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super(Dataset, self).__init__(size=length)
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self.predict = predict
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self.label = label
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self.index = 0
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self.length = length
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self.input_num = input_num
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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if self.input_num == 2:
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return (self.predict, self.label)
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return (self.predict,)
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def reset(self):
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self.index = 0
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class PReLUNet(nn.Cell):
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def __init__(self):
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super(PReLUNet, self).__init__()
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self.prelu = PReLU(channel=256)
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def construct(self, x):
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x = self.prelu(x)
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return x
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def reshape_common(parallel_mode):
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
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predict = Tensor(np.ones([32, 256]), dtype=ms.float32)
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label = Tensor(np.ones([32]), dtype=ms.int32)
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dataset = Dataset(predict, label, 2)
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net = PReLUNet()
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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model = Model(net, loss, opt)
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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def test_prelu_cell():
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"""
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Feature: distribute operator prelu in auto parallel.
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Description: prelu net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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reshape_common(ParallelMode.SEMI_AUTO_PARALLEL)
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